{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/flax/blob/main/docs_nnx/mnist_tutorial.ipynb)\n",
    "[![Open On GitHub](https://img.shields.io/badge/Open-on%20GitHub-blue?logo=GitHub)](https://github.com/google/flax/blob/main/docs_nnx/mnist_tutorial.ipynb)\n",
    "\n",
    "# MNIST tutorial\n",
    "\n",
    "Welcome to Flax NNX! In this tutorial you will learn how to build and train a simple convolutional neural network (CNN) to classify handwritten digits on the MNIST dataset using the Flax NNX API.\n",
    "\n",
    "Flax NNX is a Python neural network library built upon [JAX](https://github.com/jax-ml/jax). If you have used the Flax Linen API before, check out [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html). You should have some knowledge of the main concepts of deep learning.\n",
    "\n",
    "Let’s get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## 1. Install Flax\n",
    "\n",
    "If `flax` is not installed in your Python environment, use `pip` to install the package from PyPI (below, just uncomment the code in the cell if you are working from Google Colab/Jupyter Notebook):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install flax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## 2. Load the MNIST dataset\n",
    "\n",
    "First, you need to load the MNIST dataset and then prepare the training and testing sets via Tensorflow Datasets (TFDS). You normalize image values, shuffle the data and divide it into batches, and prefetch samples to enhance performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds  # TFDS to download MNIST.\n",
    "import tensorflow as tf  # TensorFlow / `tf.data` operations.\n",
    "\n",
    "tf.random.set_seed(0)  # Set the random seed for reproducibility.\n",
    "\n",
    "train_steps = 1200\n",
    "eval_every = 200\n",
    "batch_size = 32\n",
    "\n",
    "train_ds: tf.data.Dataset = tfds.load('mnist', split='train')\n",
    "test_ds: tf.data.Dataset = tfds.load('mnist', split='test')\n",
    "\n",
    "train_ds = train_ds.map(\n",
    "  lambda sample: {\n",
    "    'image': tf.cast(sample['image'], tf.float32) / 255,\n",
    "    'label': sample['label'],\n",
    "  }\n",
    ")  # normalize train set\n",
    "test_ds = test_ds.map(\n",
    "  lambda sample: {\n",
    "    'image': tf.cast(sample['image'], tf.float32) / 255,\n",
    "    'label': sample['label'],\n",
    "  }\n",
    ")  # Normalize the test set.\n",
    "\n",
    "# Create a shuffled dataset by allocating a buffer size of 1024 to randomly draw elements from.\n",
    "train_ds = train_ds.repeat().shuffle(1024)\n",
    "# Group into batches of `batch_size` and skip incomplete batches, prefetch the next sample to improve latency.\n",
    "train_ds = train_ds.batch(batch_size, drop_remainder=True).take(train_steps).prefetch(1)\n",
    "# Group into batches of `batch_size` and skip incomplete batches, prefetch the next sample to improve latency.\n",
    "test_ds = test_ds.batch(batch_size, drop_remainder=True).prefetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "## 3. Define the model with Flax NNX\n",
    "\n",
    "Create a CNN for classification with Flax NNX by subclassing `nnx.Module`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6",
   "metadata": {},
   "outputs": [
    {
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_79fdedab11444712830d108f7c0b1793\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from flax import nnx  # The Flax NNX API.\n",
    "from functools import partial\n",
    "from typing import Optional\n",
    "\n",
    "class CNN(nnx.Module):\n",
    "  \"\"\"A simple CNN model.\"\"\"\n",
    "\n",
    "  def __init__(self, *, rngs: nnx.Rngs):\n",
    "    self.conv1 = nnx.Conv(1, 32, kernel_size=(3, 3), rngs=rngs)\n",
    "    self.batch_norm1 = nnx.BatchNorm(32, rngs=rngs)\n",
    "    self.dropout1 = nnx.Dropout(rate=0.025)\n",
    "    self.conv2 = nnx.Conv(32, 64, kernel_size=(3, 3), rngs=rngs)\n",
    "    self.batch_norm2 = nnx.BatchNorm(64, rngs=rngs)\n",
    "    self.avg_pool = partial(nnx.avg_pool, window_shape=(2, 2), strides=(2, 2))\n",
    "    self.linear1 = nnx.Linear(3136, 256, rngs=rngs)\n",
    "    self.dropout2 = nnx.Dropout(rate=0.025)\n",
    "    self.linear2 = nnx.Linear(256, 10, rngs=rngs)\n",
    "\n",
    "  def __call__(self, x, rngs: Optional[nnx.Rngs] = None):\n",
    "    x = self.avg_pool(nnx.relu(self.batch_norm1(self.dropout1(self.conv1(x), rngs=rngs))))\n",
    "    x = self.avg_pool(nnx.relu(self.batch_norm2(self.conv2(x))))\n",
    "    x = x.reshape(x.shape[0], -1)  # flatten\n",
    "    x = nnx.relu(self.dropout2(self.linear1(x), rngs=rngs))\n",
    "    x = self.linear2(x)\n",
    "    return x\n",
    "\n",
    "# Instantiate the model.\n",
    "model = CNN(rngs=nnx.Rngs(0))\n",
    "# Visualize it.\n",
    "nnx.display(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "### Run the model\n",
    "\n",
    "Let's put the CNN model to the test!  Here, you’ll perform a forward pass with arbitrary data and print the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ 0.11409501,  0.4546129 , -0.6421267 , -0.12122799, -0.22859162,\n",
       "         0.13616608,  1.0126765 , -0.03625144,  0.6132787 , -0.06018351]],      dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jax.numpy as jnp  # JAX NumPy\n",
    "\n",
    "y = model(jnp.ones((1, 28, 28, 1)), nnx.Rngs(0))\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "## 4. Create the optimizer and define some metrics\n",
    "\n",
    "In Flax NNX, you need to create an `nnx.Optimizer` object to manage the model's parameters and apply gradients during training. `nnx.Optimizer` receives the model's reference, so that it can update its parameters, and an [Optax](https://optax.readthedocs.io/) optimizer to define the update rules. Additionally, you will define an `nnx.MultiMetric` object to keep track of the `Accuracy` and the `Average` loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12",
   "metadata": {},
   "outputs": [
    {
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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_920e157205b44214a73cd99be5592f27\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import optax\n",
    "\n",
    "learning_rate = 0.005\n",
    "momentum = 0.9\n",
    "\n",
    "optimizer = nnx.Optimizer(\n",
    "  model, optax.adamw(learning_rate, momentum), wrt=nnx.Param\n",
    ")\n",
    "metrics = nnx.MultiMetric(\n",
    "  accuracy=nnx.metrics.Accuracy(),\n",
    "  loss=nnx.metrics.Average('loss'),\n",
    ")\n",
    "\n",
    "nnx.display(optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "## 5. Define training step functions\n",
    "\n",
    "In this section, you will define a loss function using the cross entropy loss ([`optax.softmax_cross_entropy_with_integer_labels()`](https://optax.readthedocs.io/en/latest/api/losses.html#optax.softmax_cross_entropy_with_integer_labels)) that the CNN model will optimize over.\n",
    "\n",
    "In addition to the `loss`, during training and testing you will also get the `logits`, which will be used to calculate the accuracy metric. \n",
    "\n",
    "During training - the `train_step` - you will use `nnx.value_and_grad` to compute the gradients and update the model's parameters using the `optimizer` you have already defined. And during both training and testing (the `eval_step`), the `loss` and `logits` will be used to calculate the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn(model: CNN, rngs: nnx.Rngs, batch):\n",
    "  logits = model(batch['image'], rngs)\n",
    "  loss = optax.softmax_cross_entropy_with_integer_labels(\n",
    "    logits=logits, labels=batch['label']\n",
    "  ).mean()\n",
    "  return loss, logits\n",
    "\n",
    "@nnx.jit\n",
    "def train_step(model: CNN, optimizer: nnx.Optimizer, metrics: nnx.MultiMetric, rngs: nnx.Rngs, batch):\n",
    "  \"\"\"Train for a single step.\"\"\"\n",
    "  grad_fn = nnx.value_and_grad(loss_fn, has_aux=True)\n",
    "  (loss, logits), grads = grad_fn(model, rngs, batch)\n",
    "  metrics.update(loss=loss, logits=logits, labels=batch['label'])  # In-place updates.\n",
    "  optimizer.update(model, grads)  # In-place updates.\n",
    "\n",
    "@nnx.jit\n",
    "def eval_step(model: CNN, metrics: nnx.MultiMetric, rngs: nnx.Rngs, batch):\n",
    "  loss, logits = loss_fn(model, rngs, batch)\n",
    "  metrics.update(loss=loss, logits=logits, labels=batch['label'])  # In-place updates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "In the code above, the [`nnx.jit`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.jit) transformation decorator traces the `train_step` function for just-in-time compilation with [XLA](https://www.tensorflow.org/xla), optimizing performance on hardware accelerators, such as Google TPUs and GPUs. `nnx.jit` is a \"lifted\" version of the `jax.jit` transform that allows its function input and outputs to be Flax NNX objects. Similarly, `nnx.value_and_grad ` is a lifted version of `jax.value_and_grad `. Check out [the lifted transforms guide](https://flax.readthedocs.io/en/latest/guides/transforms.html) to learn more.\n",
    "\n",
    "> **Note:** The code shows how to perform several in-place updates to the model, the optimizer, the RNG streams and the metrics, but _state updates_ were not explicitly returned. This is because Flax NNX transformations respect _reference semantics_ for Flax NNX objects, and will propagate the state updates of the objects passed as input arguments. This is a key feature of Flax NNX that allows for a more concise and readable code. You can learn more in [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html).\n",
    "\n",
    "\n",
    "## 6. Train and evaluate the model\n",
    "\n",
    "Now, you can train the CNN model using batches of data for 10 epochs, evaluate the model’s performance\n",
    "on the test set after each epoch, and log the training and testing metrics (the loss and\n",
    "the accuracy) during the process. Typically this leads to the model achieving around 99% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "metrics_history = {\n",
    "  'train_loss': [],\n",
    "  'train_accuracy': [],\n",
    "  'test_loss': [],\n",
    "  'test_accuracy': [],\n",
    "}\n",
    "\n",
    "rngs = nnx.Rngs(0)\n",
    "\n",
    "for step, batch in enumerate(train_ds.as_numpy_iterator()):\n",
    "  # Run the optimization for one step and make a stateful update to the following:\n",
    "  # - The train state's model parameters\n",
    "  # - The optimizer state\n",
    "  # - The training loss and accuracy batch metrics\n",
    "  model.train() # Switch to train mode\n",
    "  train_step(model, optimizer, metrics, rngs, batch)\n",
    "\n",
    "  if step > 0 and (step % eval_every == 0 or step == train_steps - 1):  # One training epoch has passed.\n",
    "    # Log the training metrics.\n",
    "    for metric, value in metrics.compute().items():  # Compute the metrics.\n",
    "      metrics_history[f'train_{metric}'].append(value)  # Record the metrics.\n",
    "    metrics.reset()  # Reset the metrics for the test set.\n",
    "\n",
    "    # Compute the metrics on the test set after each training epoch.\n",
    "    model.eval() # Switch to eval mode\n",
    "    for test_batch in test_ds.as_numpy_iterator():\n",
    "      eval_step(model, metrics, rngs, test_batch)\n",
    "\n",
    "    # Log the test metrics.\n",
    "    for metric, value in metrics.compute().items():\n",
    "      metrics_history[f'test_{metric}'].append(value)\n",
    "    metrics.reset()  # Reset the metrics for the next training epoch.\n",
    "\n",
    "    clear_output(wait=True)\n",
    "    # Plot loss and accuracy in subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    ax1.set_title('Loss')\n",
    "    ax2.set_title('Accuracy')\n",
    "    for dataset in ('train', 'test'):\n",
    "      ax1.plot(metrics_history[f'{dataset}_loss'], label=f'{dataset}_loss')\n",
    "      ax2.plot(metrics_history[f'{dataset}_accuracy'], label=f'{dataset}_accuracy')\n",
    "    ax1.legend()\n",
    "    ax2.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25",
   "metadata": {},
   "source": [
    "## 7. Perform inference on the test set\n",
    "\n",
    "Create a `jit`-compiled model inference function (with `nnx.jit`) - `pred_step` - to generate predictions on the test set using the learned model parameters. This will enable you to visualize test images alongside their predicted labels for a qualitative assessment of model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval() # Switch to evaluation mode.\n",
    "\n",
    "@nnx.jit\n",
    "def pred_step(model: CNN, batch):\n",
    "  logits = model(batch['image'])\n",
    "  return logits.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6cb81f",
   "metadata": {},
   "source": [
    "We call .eval() before inference so Dropout is disabled and BatchNorm uses stored running stats. It is used during inference to suppress gradients and ensure deterministic, resource-efficient output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_batch = test_ds.as_numpy_iterator().next()\n",
    "pred = pred_step(model, test_batch)\n",
    "\n",
    "fig, axs = plt.subplots(5, 5, figsize=(12, 12))\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "  ax.imshow(test_batch['image'][i, ..., 0], cmap='gray')\n",
    "  ax.set_title(f'label={pred[i]}')\n",
    "  ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28",
   "metadata": {},
   "source": [
    "Congratulations! You have learned how to use Flax NNX to build and train a simple classification model end-to-end on the MNIST dataset.\n",
    "\n",
    "Next, check out [Why Flax NNX?](https://flax.readthedocs.io/en/latest/why.html) and get started with a series of [Flax NNX Guides](https://flax.readthedocs.io/en/latest/guides/index.html)."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "jupytext": {
   "formats": "ipynb,md:myst",
   "main_language": "python"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
